app.py

import streamlit as st
import PyPDF2
import io
import os
import re
import nltk
from nltk.corpus import words

# Download the words corpus if not already downloaded
nltk.download('words')

# Create a set of English words for quick lookup
english_words_set = set(words.words())

# Your existing mappings
unicodeatoz = ["ब", "द", "अ", "म", "भ", "ा", "न", "ज", "ष्", "व", "प", "ि", "फ", "ल", "य", "उ", "त्र", "च", "क", "त", "ग", "ख", "ध", "ह", "थ", "श"]
unicodeAtoZ = ["ब्", "ध", "ऋ", "म्", "भ्", "ँ", "न्", "ज्", "क्ष्", "व्", "प्", "ी", "ः", "ल्", "इ", "ए", "त्त", "च्", "क्", "त्", "ग्", "ख्", "ध्", "ह्", "थ्", "श्"]
unicode0to9 = ["ण्", "ज्ञ", "द्द", "घ", "द्ध", "छ", "ट", "ठ", "ड", "ढ"]
symbolsDict = {
    "~": "ञ्",
    "`": "ञ",
    "!": "१",
    "@": "२",
    "#": "३",
    "$": "४",
    "%": "५",
    "^": "६",
    "&": "७",
    "*": "८",
    "(": "९",
    ")": "०",
    "-": "(",
    "_": ")",
    "+": "ं",
    "[": "ृ",
    "{": "र्",
    "]": "े",
    "}": "ै",
    "\\": "्",
    "|": "्र",
    ";": "स",
    ":": "स्",
    "'": "ु",
    "\"": "ू",
    ",": ",",
    "<": "?",
    ".": "।",
    ">": "श्र",
    "/": "र",
    "?": "रु",
    "=": ".",
    "ˆ": "फ्",
    "Î": "ङ्ख",
    "å": "द्व",
    "÷": "/"
}

def normalizePreeti(preetitxt):
    normalized = ''
    previoussymbol = ''
    preetitxt = preetitxt.replace('qm', 's|')
    preetitxt = preetitxt.replace('f]', 'ो')
    preetitxt = preetitxt.replace('km', 'फ')
    preetitxt = preetitxt.replace('0f', 'ण')
    preetitxt = preetitxt.replace('If', 'क्ष')
    preetitxt = preetitxt.replace('if', 'ष')
    preetitxt = preetitxt.replace('cf', 'आ')
    index = -1
    while index + 1 < len(preetitxt):
        index += 1
        character = preetitxt[index]
        try:
            if preetitxt[index + 2] == '{':
                if preetitxt[index + 1] == 'f' or preetitxt[index + 1] == 'ो':
                    normalized += '{' + character + preetitxt[index + 1]
                    index += 2
                    continue
            if preetitxt[index + 1] == '{':
                if character != 'f':
                    normalized += '{' + character
                    index += 1
                    continue
        except IndexError:
            pass
        if character == 'l':
            previoussymbol = 'l'
            continue
        else:
            normalized += character + previoussymbol
            previoussymbol = ''
    return normalized

def is_english_word(word):
    # Remove punctuation and convert to lowercase
    word_clean = re.sub(r'\W+', '', word).lower()
    return word_clean in english_words_set

def convert(preeti):
    converted = ''
    normalizedpreeti = normalizePreeti(preeti)
    
    # Split the text into tokens (words and non-words)
    tokens = re.findall(r'\w+|\W+', normalizedpreeti)
    
    for token in tokens:
        if re.match(r'\w+', token):
            # This is a word
            if is_english_word(token):
                # English word, skip conversion
                converted += token
            else:
                # Convert the word
                converted_word = ''
                for index, character in enumerate(token):
                    try:
                        if ord(character) >= 97 and ord(character) <= 122:
                            converted_word += unicodeatoz[ord(character) - 97]
                        elif ord(character) >= 65 and ord(character) <= 90:
                            converted_word += unicodeAtoZ[ord(character) - 65]
                        elif ord(character) >= 48 and ord(character) <= 57:
                            converted_word += unicode0to9[ord(character) - 48]
                        else:
                            converted_word += symbolsDict[character]
                    except KeyError:
                        converted_word += character
                converted += converted_word
        else:
            # Non-word token (punctuation, whitespace)
            converted += token
    return converted

def extract_text_from_pdf(pdf_file):
    text = ''
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        for page in reader.pages:
            text += page.extract_text()
    return text

def process_file(inputfile):
    ext = os.path.splitext(inputfile)[1].lower()
    if ext == '.pdf':
        preeti = extract_text_from_pdf(inputfile)
    else:
        with open(inputfile, "r") as fp:
            preeti = fp.read()
    return convert(preeti)

def main():
    st.title("PDF/TXT to Unicode Converter")

    uploaded_file = st.file_uploader("Choose a PDF or TXT file", type=["pdf", "txt"])

    if uploaded_file is not None:
        file_extension = os.path.splitext(uploaded_file.name)[1].lower()

        if file_extension == ".pdf":
            pdf_reader = PyPDF2.PdfReader(io.BytesIO(uploaded_file.read()))
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text()
        else:  # .txt file
            text = uploaded_file.getvalue().decode("utf-8")

        converted_text = convert(text)

        st.subheader("Original Text")
        st.text_area("", value=text, height=200)

        st.subheader("Converted Text")
        st.text_area("", value=converted_text, height=200)

        # Create a download button for the converted text
        st.download_button(
            label="Download Converted Text",
            data=converted_text.encode("utf-8"),
            file_name="converted_text.txt",
            mime="text/plain"
        )

if __name__ == "__main__":
    main()